| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Govekar, Edvard
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2024Influence of the laser-beam intensity distribution on the performance of directed energy deposition of an axially fed metal powdercitations
- 2022Powder particle–wall collision-based design of the discrete axial nozzle-exit shape in direct laser depositioncitations
- 2022Characterization of biocomposites and glass fiber epoxy composites based on acoustic emission signals, deep feature extraction, and machine learning ; Izpeljava globokih značilk na osnovi signalov AE za karakterizacijo obremenjenih epoksidnih kompozitov iz ogljikovih vlaken in epoksidnih kompozitov iz steklenih vlaken.citations
- 2022Deep feature extraction based on AE signals for the characterization of loaded carbon fiber epoxy and glass fiber epoxy compositescitations
- 2022Characterization of Biocomposites and Glass Fiber Epoxy Composites Based on Acoustic Emission Signals, Deep Feature Extraction, and Machine Learningcitations
- 2018Annular laser beam cladding process feasibility studycitations
- 2018Annular laser beam based direct metal depositioncitations
- 2018Drop on demand generation from a metal wire by means of an annular laser beamcitations
- 2018High-speed camera thermometry of laser droplet generationcitations
- 2018Detection and characterization of stainless steel SCC by the analysis of crack related acoustic emissioncitations
Places of action
| Organizations | Location | People |
|---|
article
Deep feature extraction based on AE signals for the characterization of loaded carbon fiber epoxy and glass fiber epoxy composites
Abstract
Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for carbon fiber epoxy (CFE) and glass fiber epoxy (GFE) composites. The research is focused on the recognition of material structure (CFE or GFE) based on the analysis of AE signals. We propose the extraction of deep features using a convolutional autoencoder (CAE). The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through decision trees and discriminant analysis, combined with feature selection, to estimate the predictive potential of various feature sets. Results show that the application of deep features increases recognition accuracy. By using only standard AE-based features, a classification accuracy of around 80% is obtained, and adding deep features improves the classification accuracy to above 90%. Consequently, the application of deep feature extraction is encouraged for the characterization of loaded CFE composites.